Journal
IEEE ACCESS
Volume 8, Issue -, Pages 227340-227348Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2020.3044973
Keywords
Artificial neural networks; Heat transfer; Numerical models; Mathematical model; Training; Computational modeling; Convergence; Squeezing flow; heat transfer; soft computing infrastructure; neural networks backpropagated; Levenberg-Marquard training
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Funding
- Deanship of Scientific Research (DSR), King Abdulaziz University [KEP-Msc-17-130-41]
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In this paper, the computational strength in terms of soft computing neural networks backpropagated with the efficacy of Levenberg-Marquard training (NN-BLMT) is presented to study the squeezing flow with the heat transfer model (SF-HTM). The governing system of PDEs is reduced to an equivalent system of nonlinear ODEs using similarity transformations. NN-BLMT dataset for all problem scenarios progresses through the standard Adam numerical method by the influence of Prandtl number, Eckert number, and thermal slip. The processing of NN-BLMT training, testing, and validation, is employed for various scenarios and cases to find and compare approximation solutions with reference results. For the fluidic system SF-HTM, convergence analysis based on mean square errors, histogram presentations, and statistical regression plots is considered for the proposed computing infrastructure's performance in terms of NN-BLMT. Matching of the results for the fluid flow system SF-HTM based on proposed and reference results in terms of convergence up-to 10(-07) to 10(-03) proves the worth of proposed NN-BLMT.
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